Evaluating parent-mediated interventions to support child emotion regulation: a review of preventative approaches across childhood
Bibliographic record
Abstract
Parenting plays an important role in children’s emotion regulation (ER) development. Parent-mediated interventions, preventative approaches that equip caregivers with skills to enhance their children’s emotional, behavioural, or developmental outcomes, have emerged as a promising strategy to address difficulties in child ER, particularly during the early to middle childhood years ( Morawska et al., 2019 ). This narrative review synthesized studies evaluating the effectiveness of parent-mediated preventative interventions aimed at enhancing ER in children aged 0–12 years. A search across three databases yielded 3189 articles, with 24 studies retained after applying inclusion criteria. Key interventions included Tuning in to Kids, Incredible Years, Parent-Child Interaction Therapy, Family Check-Up, and Triple P – Positive Parenting Program. While the interventions varied in design, all included a parental and child ER element (albeit using varied terminology to describe it), providing caregivers with skills to regulate their emotions, engage in effective parenting practices, and model healthy ER behaviours. Despite these promising outcomes, significant methodological limitations, such as inconsistent and imprecise measurement of ER and limited research on middle childhood, restricted a comprehensive understanding of the impact of these interventions. Future research should incorporate rigorous and comprehensive ER assessments and expand the focus of inquiry to include children in middle childhood to addressing the identified gaps in current literature and further promote adaptive ER development in children.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".